A simplified spatial+ approach to mitigate spatial confounding in multivariate spatial areal models

Urdangarin Iztueta, Arantxa; Goicoa Mangado, Tomás; Kneib, Thomas; Ugarte Martínez, María Dolores

Publication:
A simplified spatial+ approach to mitigate spatial confounding in multivariate spatial areal models

dc.contributor.author	Urdangarin Iztueta, Arantxa
dc.contributor.author	Goicoa Mangado, Tomás
dc.contributor.author	Kneib, Thomas
dc.contributor.author	Ugarte Martínez, María Dolores
dc.contributor.department	Estadística, Informática y Matemáticas	es_ES
dc.contributor.department	Estatistika, Informatika eta Matematika	eu
dc.contributor.department	Institute for Advanced Materials and Mathematics - INAMAT2	en
dc.date.accessioned	2024-05-24T11:10:31Z
dc.date.available	2024-05-24T11:10:31Z
dc.date.issued	2024
dc.date.updated	2024-05-24T11:03:09Z
dc.description.abstract	Spatial areal models encounter the well-known and challenging problem of spatial confounding. This issue makes it arduous to distinguish between the impacts of observed covariates and spatial random effects. Despite previous research and various proposed methods to tackle this problem, finding a definitive solution remains elusive. In this paper, we propose a simplified version of the spatial+ approach that involves dividing the covariate into two components. One component captures large-scale spatial dependence, while the other accounts for short-scale dependence. This approach eliminates the need to separately fit spatial models for the covariates. We apply this method to analyse two forms of crimes against women, namely rapes and dowry deaths, in Uttar Pradesh, India, exploring their relationship with socio-demographic covariates. To evaluate the performance of the new approach, we conduct extensive simulation studies under different spatial confounding scenarios. The results demonstrate that the proposed method provides reliable estimates of fixed effects and posterior correlations between different responses.	en
dc.description.sponsorship	This work has been supported by Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033.	en
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Urdangarin, A., Goicoa, T., Kneib, T., Ugarte, M. D. (2024) A simplified spatial+ approach to mitigate spatial confounding in multivariate spatial areal models. Spatial Statistics, 59, 1-18. https://doi.org/10.1016/j.spasta.2023.100804.	es_ES
dc.identifier.doi	10.1016/j.spasta.2023.100804
dc.identifier.issn	2211-6753
dc.identifier.uri	https://academica-e.unavarra.es/handle/2454/48189
dc.language.iso	eng	en
dc.publisher	Elsevier	en
dc.relation.ispartof	Spatial Statistics (2024), vol. 59, 100804	es_ES
dc.relation.projectID	info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113125RB-I00/ES/	en
dc.relation.publisherversion	https://doi.org/10.1016/j.spasta.2023.100804
dc.rights	© 2024 The Authors. This is an open access article under the CC BY-NC-ND license.	en
dc.rights.accessRights	info:eu-repo/semantics/openAccess
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject	Crimes against women	en
dc.subject	M-models	en
dc.subject	Spatial confounding	en
dc.subject	Spatial+	en
dc.title	A simplified spatial+ approach to mitigate spatial confounding in multivariate spatial areal models	en
dc.type	info:eu-repo/semantics/article
dc.type.version	Versión publicada / Argitaratu den bertsioa	es
dc.type.version	info:eu-repo/semantics/publishedVersion	en
dspace.entity.type	Publication
relation.isAuthorOfPublication	73ddf3f0-4c5d-426b-b308-e89dbbb1c884
relation.isAuthorOfPublication	77ba75f3-a30d-4f01-8cc5-9de6d3a10d8d
relation.isAuthorOfPublication	e87ff19e-9d36-4286-989b-cafd391dff9d
relation.isAuthorOfPublication.latestForDiscovery	73ddf3f0-4c5d-426b-b308-e89dbbb1c884